Machine Learning With Scikit-Learn | Scikit-Learn Tutorial | Python Tutorial | SimpliCode

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This Machine Learning With Scikit-Learn tutorial will help you understand what is Scikit-learn, what we can achieve using Scikit-learn, and a demo on how to use Scikit-learn in Python. In this Scikit-Learn Tutorial, we will explore built-in libraries, and built-in functions and perform data analysis and data extraction.

Scikit-Learn tutorial content:
00:00 Introduction
01:19 What is Scikit-learn?
01:51 What we can achieve using Scikit-learn
06:15 Import Libraries
09:00 Load Dataset
10:17 Hands-on Demonstration

✅Scikit is a powerful and modern machine-learning python library. It's a great tool for fully and semi-automated advanced data analysis and information extraction. There are a lot of reasons why Scikit-Learn is a preferred machine learning tool. It has efficient tools to identify and organize problems, such as whether it fits a supervised or unsupervised learning model. It contains many free and open data sets. It has a rich set of built-in libraries for learning and predicting. It provides model support for every problem type. It also has built-in functions such as pickle for model persistence. It is supported by a huge open-source community and vendor base.

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✅ About Artificial Intelligence Engineer Master's Program

The Artificial Intelligence course, created in partnership with IBM, introduces students to blended learning and prepares them to be specialists in AI and Data Science. IBM, located in Armonk, New York, is a significant cognitive service and integrated cloud solution firm that provides many technology and consulting solutions. IBM invests $6 billion in research & development every year and has won many Awards and recognition.

✅ What skills are covered in this Artificial Intelligence course?

You will be able to demonstrate the following ability after completing this Artificial Intelligence certification training:

- Learn Artificial Intelligence's concept, purpose, domain breadth, phases, implementations, and impacts.
- Create real-world projects, games, prediction models, logic constraint satisfaction concerns, experience and understanding systems, probabilistic models, and agent decision-making skills using your artificial intelligent machines and models.
- Learn basic programming features and technicalities, such as data types, tuples, lists, arrays, basic operators, and functions.
- Learn how to develop your Python programs and use the Jupyter notebook to analyze fundamental data.
- and many more

✅ Tools Covered

- NumPy
- Pandas
- SciPy
- Apache Spark
- Keras
- Tensor Flow
- Python
- R Programming
- Scala

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